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updated references, fixes #157
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‎notebooks/00-pentapeptide-showcase.ipynb‎

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"5. [PCCA and TPT analysis 📓](05-pcca-tpt.ipynb)\n",
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"6. [Hidden Markov state models (HMMs) 📓](06-hidden-markov-state-models.ipynb).\n",
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"7. [Expectations and observables 📓](07-expectations-and-observables.ipynb)\n",
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"8. [Common problems & bad data situations 📓](08-common-problems.ipynb)\n",
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"\n",
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"8. [Common problems & bad data situations 📓](08-common-problems.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References\n",
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"\n",
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"<a id=\"cite-gmrq\"/><sup><a href=#ref-1>[^]</a></sup>Robert T. McGibbon and Vijay S. Pande. 2015. _Variational cross-validation of slow dynamical modes in molecular kinetics_. [URL](https://doi.org/10.1063/1.4916292)\n",
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"\n",
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"<a id=\"cite-vamp-preprint\"/><sup><a href=#ref-2>[^]</a></sup>Wu, H. and No&eacute;, F.. 2017. _Variational approach for learning Markov processes from time series data_.\n",
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"<a id=\"cite-vamp-preprint\"/><sup><a href=#ref-2>[^]</a></sup>Wu, H. and Noé, F.. 2017. _Variational approach for learning Markov processes from time series data_. [URL](https://arxiv.org/pdf/1707.04659.pdf)\n",
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"\n",
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"<a id=\"cite-vampnet\"/><sup><a href=#ref-3>[^]</a></sup>Mardt, A. and Pasquali, L. and Wu, H. and No&eacute;, F.. 2017. _VAMPnets: Deep learning of molecular kinetics_.\n",
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"<a id=\"cite-vampnet\"/><sup><a href=#ref-3>[^]</a></sup>Andreas Mardt and Luca Pasquali and Hao Wu and Frank Noé. 2018. _VAMPnets for deep learning of molecular kinetics_. [URL](https://doi.org/10.1038/s41467-017-02388-1)\n",
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"\n",
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"<a id=\"cite-tica2\"/><sup><a href=#ref-4>[^]</a></sup>Molgedey, L. and Schuster, H. G.. 1994. _Separation of a mixture of independent signals using time delayed correlations_. [URL](http://dx.doi.org/10.1103/PhysRevLett.72.3634)\n",
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"\n",
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"<a id=\"cite-tica\"/><sup><a href=#ref-5>[^]</a></sup>Guillermo Pérez-Hernández and Fabian Paul and Toni Giorgino and Gianni De Fabritiis and Frank Noé. 2013. _Identification of slow molecular order parameters for Markov model construction_. [URL](https://doi.org/10.1063/1.4811489)\n",
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"\n",
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"<a id=\"cite-msm-jhp\"/><sup><a href=#ref-6>[^]</a></sup>Prinz, Jan-Hendrik and Wu, Hao and Sarich, Marco and Keller, Bettina and Senne, Martin and Held, Martin and Chodera, John D. and Sch&uuml;tte, Christof and No&eacute;, Frank. 2011. _Markov models of molecular kinetics: Generation and validation_. [URL](http://scitation.aip.org/content/aip/journal/jcp/134/17/10.1063/1.3565032)\n",
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"<a id=\"cite-msm-jhp\"/><sup><a href=#ref-6>[^]</a></sup>Prinz, Jan-Hendrik and Wu, Hao and Sarich, Marco and Keller, Bettina and Senne, Martin and Held, Martin and Chodera, John D. and Schütte, Christof and Noé, Frank. 2011. _Markov models of molecular kinetics: Generation and validation_. [URL](http://scitation.aip.org/content/aip/journal/jcp/134/17/10.1063/1.3565032)\n",
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"\n",
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"<a id=\"cite-swope-its\"/><sup><a href=#ref-7>[^]</a></sup>William C. Swope and Jed W. Pitera and Frank Suits. 2004. _Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory\\textdagger_. [URL](https://doi.org/10.1021/jp037421y)\n",
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"\n",
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"<a id=\"cite-pcca_plus_plus\"/><sup><a href=#ref-8>[^]</a></sup>Susanna Röblitz and Marcus Weber. 2013. _Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification_. [URL](https://doi.org/10.1007/s11634-013-0134-6)\n",
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"\n",
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"<a id=\"cite-mdtraj\"/><sup><a href=#ref-9>[^]</a></sup>McGibbon, Robert T. and Beauchamp, Kyle A. and Harrigan, Matthew P. and Klein, Christoph and Swails, Jason M. and Hern&aacute;ndez, Carlos X. and Schwantes, Christian R. and Wang, Lee-Ping and Lane, Thomas J. and Pande, Vijay S.. 2015. _MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories_.\n",
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"<a id=\"cite-mdtraj\"/><sup><a href=#ref-9>[^]</a></sup>McGibbon, Robert T. and Beauchamp, Kyle A. and Harrigan, Matthew P. and Klein, Christoph and Swails, Jason M. and Hernández, Carlos X. and Schwantes, Christian R. and Wang, Lee-Ping and Lane, Thomas J. and Pande, Vijay S.. 2015. _MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories_.\n",
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"\n",
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"<a id=\"cite-simon-amm\"/><sup><a href=#ref-10>[^]</a></sup>Simon Olsson and Hao Wu and Fabian Paul and Cecilia Clementi and Frank Noé. 2017. _Combining experimental and simulation data of molecular processes via augmented Markov models_. [URL](https://doi.org/10.1073/pnas.1704803114)\n",
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"\n",
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"<a id=\"cite-noe-dy-neut-scatt\"/><sup><a href=#ref-13>[^]</a></sup>Benjamin Lindner and Zheng Yi and Jan-Hendrik Prinz and Jeremy C. Smith and Frank Noé. 2013. _Dynamic neutron scattering from conformational dynamics. I. Theory and Markov models_. [URL](https://doi.org/10.1063/1.4824070)\n",
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"\n",
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"<a id=\"cite-hmm-baum-welch-alg\"/><sup><a href=#ref-14>[^]</a></sup>Leonard E. Baum and Ted Petrie and George Soules and Norman Weiss. 1970. _A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains_. [URL](http://www.jstor.org/stable/2239727)\n",
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"\n",
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"\n"
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]
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}
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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"version": "3.6.6"
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},
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"toc": {
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"base_numbering": 1,

‎notebooks/01-data-io-and-featurization.ipynb‎

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"- `pyemma.coordinates.vamp().score()` to score the quality of the features,\n",
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"- `pyemma.plots.plot_feature_histograms()` to show the distributions of all loaded features,\n",
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"- `pyemma.plots.plot_density()` to visualize the sample density, and\n",
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"- `pyemma.plots.plot_free_energy()` to visualize the free energy surface of two selected features.\n",
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"\n",
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"- `pyemma.plots.plot_free_energy()` to visualize the free energy surface of two selected features."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References\n",
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"\n",
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"<a id=\"cite-vamp-preprint\"/><sup><a href=#ref-1>[^]</a></sup>Wu, H. and No&eacute;, F.. 2017. _Variational approach for learning Markov processes from time series data_.\n",
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"<a id=\"cite-vamp-preprint\"/><sup><a href=#ref-1>[^]</a></sup>Wu, H. and Noé, F.. 2017. _Variational approach for learning Markov processes from time series data_. [URL](https://arxiv.org/pdf/1707.04659.pdf)\n",
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"\n"
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]
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}

‎notebooks/02-dimension-reduction-and-discretization.ipynb‎

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"- `pyemma.coordinates.tica()` to perform a time-lagged independent component analysis, and\n",
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"- `pyemma.coordinates.vamp()` to analyze the quality of some feature spaces, perform dimension reduction, and\n",
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"- `pyemma.coordinates.cluster_kmeans()` to perform a $k$-means clustering, and\n",
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"- `pyemma.coordinates.cluster_regspace()` to perform a regspace clustering.\n",
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"\n",
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"- `pyemma.coordinates.cluster_regspace()` to perform a regspace clustering."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References\n",
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"\n",
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"<a id=\"cite-tica2\"/><sup><a href=#ref-1>[^]</a></sup>Molgedey, L. and Schuster, H. G.. 1994. _Separation of a mixture of independent signals using time delayed correlations_. [URL](http://dx.doi.org/10.1103/PhysRevLett.72.3634)\n",
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"\n",
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"<a id=\"cite-tica\"/><sup><a href=#ref-2>[^]</a></sup>Guillermo Pérez-Hernández and Fabian Paul and Toni Giorgino and Gianni De Fabritiis and Frank Noé. 2013. _Identification of slow molecular order parameters for Markov model construction_. [URL](https://doi.org/10.1063/1.4811489)\n",
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"\n",
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"<a id=\"cite-vamp-preprint\"/><sup><a href=#ref-3>[^]</a></sup>Wu, H. and No&eacute;, F.. 2017. _Variational approach for learning Markov processes from time series data_.\n",
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"<a id=\"cite-vamp-preprint\"/><sup><a href=#ref-3>[^]</a></sup>Wu, H. and Noé, F.. 2017. _Variational approach for learning Markov processes from time series data_. [URL](https://arxiv.org/pdf/1707.04659.pdf)\n",
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"\n",
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"<a id=\"cite-aggarwal_surprising_2001\"/><sup><a href=#ref-4>[^]</a></sup>Aggarwal, Charu C. and Hinneburg, Alexander and Keim, Daniel A.. 2001. _On the Surprising Behavior of Distance Metrics in High Dimensional Space_.\n",
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"\n"
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]
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}

‎notebooks/03-msm-estimation-and-validation.ipynb‎

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"- `pyemma.msm.bayesian_markov_model()` to estimate a Bayesian MSM,\n",
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"- the `timescales()` method of an estimated MSM object to access its implied timescales,\n",
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"- the `cktest()` method of an estimated MSM object to perform a Chapman-Kolmogorow test, and\n",
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"- `pyemma.plots.plot_cktest()` to visualize the latter.\n",
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"\n",
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"- `pyemma.plots.plot_cktest()` to visualize the latter."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References\n",
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"\n",
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"<a id=\"cite-msm-jhp\"/><sup><a href=#ref-1>[^]</a></sup>Prinz, Jan-Hendrik and Wu, Hao and Sarich, Marco and Keller, Bettina and Senne, Martin and Held, Martin and Chodera, John D. and Sch&uuml;tte, Christof and No&eacute;, Frank. 2011. _Markov models of molecular kinetics: Generation and validation_. [URL](http://scitation.aip.org/content/aip/journal/jcp/134/17/10.1063/1.3565032)\n",
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"<a id=\"cite-msm-jhp\"/><sup><a href=#ref-1>[^]</a></sup>Prinz, Jan-Hendrik and Wu, Hao and Sarich, Marco and Keller, Bettina and Senne, Martin and Held, Martin and Chodera, John D. and Schütte, Christof and Noé, Frank. 2011. _Markov models of molecular kinetics: Generation and validation_. [URL](http://scitation.aip.org/content/aip/journal/jcp/134/17/10.1063/1.3565032)\n",
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"\n",
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"<a id=\"cite-msm-book\"/><sup><a href=#ref-2>[^]</a></sup>Gregory R. Bowman and Vijay S. Pande and Frank Noé. 2014. _An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation_. [URL](https://doi.org/10.1007%2F978-94-007-7606-7)\n",
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"\n",
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"<a id=\"cite-msm-brooke\"/><sup><a href=#ref-3>[^]</a></sup>Brooke E. Husic and Vijay S. Pande. 2018. _Markov State Models: From an Art to a Science_.\n",
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"\n",
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"\n"
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}

‎notebooks/04-msm-analysis.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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"version": "3.6.6"
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},
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"toc": {
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"base_numbering": 1,

‎notebooks/05-pcca-tpt.ipynb‎

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"\n",
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"For visualizing MSMs or kinetic networks we used\n",
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"- `pyemma.plots.plot_density()`, `pyemma.plots.plot_contour()` and\n",
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"- `pyemma.plots.plot_network()`.\n",
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"- `pyemma.plots.plot_network()`."
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References\n",
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"\n",
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"<a id=\"cite-pcca_plus_plus\"/><sup><a href=#ref-1>[^]</a><a href=#ref-4>[^]</a></sup>Susanna Röblitz and Marcus Weber. 2013. _Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification_. [URL](https://doi.org/10.1007/s11634-013-0134-6)\n",
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"\n",
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"<a id=\"cite-weinan-tpt\"/><sup><a href=#ref-2>[^]</a></sup>Weinan E. and Eric Vanden-Eijnden. 2006. _Towards a Theory of Transition Paths_. [URL](https://doi.org/10.1007/s10955-005-9003-9)\n",
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"\n",
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"<a id=\"cite-metzner-msm-tpt\"/><sup><a href=#ref-3>[^]</a></sup>Philipp Metzner and Christof Schütte and Eric Vanden-Eijnden. 2009. _Transition Path Theory for Markov Jump Processes_. [URL](https://doi.org/10.1137/070699500)\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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},
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"toc": {
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‎notebooks/06-expectations-and-observables.ipynb‎

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"* `sample_conf()` computes the confidence interval of a property over the sampled MSMs/HMMs in a Bayesian model.\n",
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"* `sample_std()` computes the standard deviation of a property over the sampled MSMs/HMMs in a Bayesian model.\n",
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"\n",
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"Finally, we have shown how to use these methods together with precomputed observables.\n",
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"\n",
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"Finally, we have shown how to use these methods together with precomputed observables."
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References\n",
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"\n",
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"<a id=\"cite-simon-amm\"/><sup><a href=#ref-1>[^]</a><a href=#ref-5>[^]</a></sup>Simon Olsson and Hao Wu and Fabian Paul and Cecilia Clementi and Frank Noé. 2017. _Combining experimental and simulation data of molecular processes via augmented Markov models_. [URL](https://doi.org/10.1073/pnas.1704803114)\n",
@@ -941,7 +946,6 @@
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"<a id=\"cite-simon-mech-mod-nmr\"/><sup><a href=#ref-3>[^]</a></sup>Simon Olsson and Frank Noé. 2016. _Mechanistic Models of Chemical Exchange Induced Relaxation in Protein NMR_. [URL](https://doi.org/10.1021/jacs.6b09460)\n",
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"\n",
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"<a id=\"cite-noe-dy-neut-scatt\"/><sup><a href=#ref-4>[^]</a></sup>Benjamin Lindner and Zheng Yi and Jan-Hendrik Prinz and Jeremy C. Smith and Frank Noé. 2013. _Dynamic neutron scattering from conformational dynamics. I. Theory and Markov models_. [URL](https://doi.org/10.1063/1.4824070)\n",
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"name": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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"version": "3.6.6"
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},
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"toc": {
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"base_numbering": 1,

‎notebooks/07-hidden-markov-state-models.ipynb‎

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"- `pyemma.msm.bayesian_hidden_markov_model()` to estimate a Bayesian HMM, \n",
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"- the `metastable_assignments` attribute of an HMM object to access the metastable membership of discrete states, \n",
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"- the `hidden_state_probabilities` attribute to assess probabilities of hidden states over time, and\n",
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"- the `hidden_state_trajectories` attribute that extracts the most likely trajectory in hidden state space.\n",
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"\n",
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"- the `hidden_state_trajectories` attribute that extracts the most likely trajectory in hidden state space."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## References\n",
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"\n",
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"<a id=\"cite-hmm-baum-welch-alg\"/><sup><a href=#ref-1>[^]</a></sup>Leonard E. Baum and Ted Petrie and George Soules and Norman Weiss. 1970. _A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains_. [URL](http://www.jstor.org/stable/2239727)\n",
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"\n",
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"<a id=\"cite-noe-proj-hid-msm\"/><sup><a href=#ref-2>[^]</a></sup>Frank Noé and Hao Wu and Jan-Hendrik Prinz and Nuria Plattner. 2013. _Projected and hidden Markov models for calculating kinetics and metastable states of complex molecules_. [URL](https://doi.org/10.1063/1.4828816)\n",
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"\n",
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"<a id=\"cite-hmm-tutorial\"/><sup><a href=#ref-3>[^]</a></sup>L.R. Rabiner. 1989. _A tutorial on hidden Markov models and selected applications in speech recognition_. [URL](https://doi.org/10.1109/5.18626)\n",
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"\n",
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"\n"
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]
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}
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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"version": "3.6.6"
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},
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"toc": {
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"base_numbering": 1,

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